February 2024 arXiv papers — page 73

Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Norbert Proske

Minimax optimization problems have attracted a lot of attention over the past few years, with applications ranging from economics to machine learning. While advanced optimization methods exist for such problems, characterizing their dynamics in stochastic scenarios remains notably challenging. In this paper, we pioneer the use of stochastic differential equa

Laura Andrea Barrero Guevara, Sarah C Kramer, Tobias Kurth, Matthieu Domenech de Cellès

A pressing question resulting from global warming is how infectious diseases will be affected by climate change. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proven difficult due to the challenges of assessing causality from the predomin

Melvin Yeung

We provide evidence that the approach of [Ilyashenko 1991] to the proof of Dulac's theorem has a gap. Although the asymptotics of [Ilyashenko 1991] capture far more than the asymptotics of Dulac, we prove that the arguments for why the asymptotics in [Ilyashenko 1991] are not themselves oscillatory is insufficient. We give an explicit counterexample and we d

Sourojit Ghosh, Sarah Coppola

Traditional and currently-prevalent pedagogies of design perpetuate ableist and exclusionary notions of what it means to be a designer. In this paper, we trace such historically exclusionary norms of design education, and highlight modern-day instances from our own experiences as design educators in such epistemologies. Towards imagining a more inclusive and

Anh-Ton Tran, Grace Guo, Jordan Taylor, Katsuki Chan

Activists, governmentsm and academics regularly advocate for more open data. But how is data made open, and for whom is it made useful and usable? In this paper, we investigate and describe the work of making eviction data open to tenant organizers. We do this through an ethnographic description of ongoing work with a local housing activist organization. Thi

Oleh Savchuk

Transverse momentum correlations were recently measured by the ALICE collaboration at the LHC. A long-range structure in terms of relative pseudorapidity of particle pairs is observed. This may imply some signal of the initial state owing to the shear spread of the correlation. However, the fluctuations inside a thermally equilibrated medium have to be taken

Phong C. H. Nguyen, Xinlun Cheng, Shahab Azarfar, Pradeep Seshadri

Modeling unsteady, fast transient, and advection-dominated physics problems is a pressing challenge for physics-aware deep learning (PADL). The physics of complex systems is governed by large systems of partial differential equations (PDEs) and ancillary constitutive models with nonlinear structures, as well as evolving state fields exhibiting sharp gradient

Thanh Dang, Lingjiong Zhu

Via a Bismut-Elworthy-Li formula from [KPP23], we derive uniform gradient estimates for transition semigroups associated with stochastic differential equations driven by a large class of cylindrical L\'{e}vy processes which includes the important case of cylindrical $\alpha$-stable processes. As the first application, we formulate a Stein's method for quanti

Ruibo Chen, Yihan Wu, Lichang Chen, Guodong Liu

Data selection in instruction tuning emerges as a pivotal process for acquiring high-quality data and training instruction-following large language models (LLMs), but it is still a new and unexplored research area for vision-language models (VLMs). Existing data selection approaches on LLMs either rely on single unreliable scores, or use downstream tasks for

Sebastian Doerrich, Tobias Archut, Francesco Di Salvo, Christian Ledig

Traditional deep learning models implicity encode knowledge limiting their transparency and ability to adapt to data changes. Yet, this adaptability is vital for addressing user data privacy concerns. We address this limitation by storing embeddings of the underlying training data independently of the model weights, enabling dynamic data modifications withou

Kim Hammar, Tao Li, Rolf Stadler, Quanyan Zhu

We study automated security response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed, non-stationary game. We relax the standard assumption that the game model is correctly specified and consider that each player has a probabilistic conjecture about the model, which may be misspecified in the

Faith Johnson, Bryan Bo Cao, Ashwin Ashok, Shubham Jain

Visual navigation follows the intuition that humans can navigate without detailed maps. A common approach is interactive exploration while building a topological graph with images at nodes that can be used for planning. Recent variations learn from passive videos and can navigate using complex social and semantic cues. However, a significant number of traini

Lucie E. Rowland, Anna F. McLeod, Azadeh Fattahi, Francesco Belfiore

Stellar feedback in dwarf galaxies remains, to date, poorly explored, yet is crucial to understanding galaxy evolution in the early Universe. In particular, pre-supernova feedback has recently been found to play a significant role in regulating and disrupting star formation in larger spiral galaxies, but it remains uncertain if it also plays this role in dwa

Panagiotis Lymperopoulos, Liping Liu

Finding Minimal Unsatisfiable Subsets (MUSes) of binary constraints is a common problem in infeasibility analysis of over-constrained systems. However, because of the exponential search space of the problem, enumerating MUSes is extremely time-consuming in real applications. In this work, we propose to prune formulas using a learned model to speed up MUS enu

Iain McDonald, Albert A. Zijlstra, Nick L. J. Cox, Emma L. Alexander

Stellar atmosphere modelling predicts the luminosity and temperature of a star, together with parameters such as the effective gravity and the metallicity, by reproducing the observed spectral energy distribution. Most observational data comes from photometric surveys, using a variety of passbands. We herein present the Python Stellar Spectral Energy Distrib

Isidora Bailly-Hall, Christine Berkesch, Karina Dovgodko, Sean Guan

For finite sets of points in $\mathbb{P}^n \times \mathbb{P}^m$, we produce short virtual resolutions, as introduced by Berkesch--Erman--Smith. We first intersect with a sufficiently high power of one set of variables for points in $\mathbb{P}^n \times \mathbb{P}^m$ to produce a virtual resolution of length $n+m$. Then, we describe an explicit virtual resolu

Kiyoshi Igusa

We give a bijection between ordered $m$-clusters and (complete) $m$-exceptional sequences, a concept that we introduce for this purpose. This holds for all hereditary artin algebras. This extends the bijection in the $m = 1$ case shown in arXiv:1706.02041.

Alan Luner, Benjamin Grimmer

This work considers the effect of averaging, and more generally extrapolation, of the iterates of gradient descent in smooth convex optimization. After running the method, rather than reporting the final iterate, one can report either a convex combination of the iterates (averaging) or a generic combination of the iterates (extrapolation). For several common

Behzad Eslam Panah

Motivated by a new model of nonlinear electrodynamics known as Modified Maxwell (ModMax) theory, an exact analytical solution for black holes is obtained by coupling ModMax nonlinear electrodynamics and $F(R)$ gravity. Then, the effects of the system's parameters ($F(R)$-ModMax gravity parameters) on the event horizons are analyzed. The obtained black holes

C. Millauro, C. R. Argüelles, F. L. Vieyro, V. Crespi

The study of the physics of accretion discs developed around the supermassive black hole (BH) candidates are essential theoretical tools to test their nature. Here, we study the accretion flow and associated emission using generalised $\alpha$-discs on to horizonless dark compact objects, in order to compare with the traditional BH scenario. The BH alternati

Marcus de Carvalho, Mahardhika Pratama, Jie Zhang, Chua Haoyan

Continual learning is a process that involves training learning agents to sequentially master a stream of tasks or classes without revisiting past data. The challenge lies in leveraging previously acquired knowledge to learn new tasks efficiently, while avoiding catastrophic forgetting. Existing methods primarily focus on single domains, restricting their ap

Raffaele Resta

The electric field of light induces--in a non centrosymmetric insulator--a dc current, quadratic in the field magnitude, and called "shift current". When addressed from a many-electron viewpoint, the shift current has a simple explanation and a simple formulation as well, deeply rooted in quantum geometry. The basic formula is then specialized to the indepen

Weihong Tang, Yun Li, Shalika Walker, Tamas Keviczky

Heat pump and thermal energy storage (HPTES) systems, which are widely utilized in modern buildings for providing domestic hot water, contribute to a large share of household electricity consumption. With the increasing integration of renewable energy sources (RES) into modern power grids, demand-side management (DSM) becomes crucial for balancing power gene

Patrick Meyfroidt, Dilini Abeygunawardane, Matthias Baumann, Adia Bey

Land use expansion is linked to major sustainability concerns including climate change, food security and biodiversity loss. This expansion is largely concentrated in so-called frontiers, defined here as places experiencing marked transformations due to rapid resource exploitation. Understanding the mechanisms shaping these frontiers is crucial for sustainab

Frank Wildenburg, Michael Hanna, Sandro Pezzelle

In everyday language use, speakers frequently utter and interpret sentences that are semantically underspecified, namely, whose content is insufficient to fully convey their message or interpret them univocally. For example, to interpret the underspecified sentence "Don't spend too much", which leaves implicit what (not) to spend, additional linguistic conte

Jingpu Yang, Zehua Han, Mengyu Xiang, Helin Wang

With the rapid advancement of Neural Machine Translation (NMT), enhancing translation efficiency and quality has become a focal point of research. Despite the commendable performance of general models such as the Transformer in various aspects, they still fall short in processing long sentences and fully leveraging bidirectional contextual information. This

Kang Cai, Prabin Parajuli, Anuvetha Govindarajan, Lin Tian

In noisy quantum systems, achieving high-fidelity state preparation using the adiabatic approach faces a dilemma: either extending the evolution time to reduce diabatic transitions or shortening it to mitigate decoherence effects. Here, we present a quantum shortcut to adiabaticity for state preparation in a finite-sized Jaynes-Cummings lattice by applying c

Guillermo Toyos-Marfurt, Petr Kuznetsov

Computability, in the presence of asynchrony and failures, is one of the central questions in distributed computing. The celebrated asynchronous computability theorem (ACT) characterizes the computing power of the read-write shared-memory model through the geometric properties of its protocol complex: a combinatorial structure describing the states the model

Nishant Balepur, Abhilasha Ravichander, Rachel Rudinger

Multiple-choice question answering (MCQA) is often used to evaluate large language models (LLMs). To see if MCQA assesses LLMs as intended, we probe if LLMs can perform MCQA with choices-only prompts, where models must select the correct answer only from the choices. In three MCQA datasets and four LLMs, this prompt bests a majority baseline in 11/12 cases,

Adam Sabra, Cyprian Wronka, Michelle Mao, Samer Hijazi

As more speech technologies rely on a supervised deep learning approach with clean speech as the ground truth, a methodology to onboard said speech at scale is needed. However, this approach needs to minimize the dependency on human listening and annotation, only requiring a human-in-the-loop when needed. In this paper, we address this issue by outlining Spe

Albert Díaz-Guilera, Dimitri Marinelli, Conrad J. Pérez-Vicente

In the field of collective dynamics, the Kuramoto model serves as a benchmark for the investigation of synchronization phenomena. While mean-field approaches and complex networks have been widely studied, the simple topology of a circle is still relatively unexplored, especially in the context of excitatory and inhibitory interactions. In this work, we focus

D. Cintas, S. Hedges, W. G. Thompson, P. An

The amount of light produced by nuclear recoils in scintillating targets is strongly quenched compared to that produced by electrons. A precise understanding of the quenching factor is particularly interesting for WIMP searches and CE{\nu}NS measurements since both rely on nuclear recoils, whereas energy calibrations are more readily accessible from electron

Johan Obando-Ceron, Aaron Courville, Pablo Samuel Castro

Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage prior insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables value-based agents to maximize parameter effectiveness. This results in networks that yield dramatic pe

Jack Carlisle

We survey some results in the field of equivariant cobordism. In particular, we use methods from equivariant stable homotopy theory to calculate the unoriented $C_2$-equivariant bordism ring $\Omega^{C_2}_*$, which was originally calculated by Alexander using other methods. Our proof method generalizes well to other settings, such as equivariant complex cobo

M. Hasan Barbhuiya, Paul A. Cassak, Subash Adhikari, Tulasi N. Parashar

A common approach to assess the nature of energy conversion in a classical fluid or plasma is to compare power densities of the various possible energy conversion mechanisms. A forefront research area is quantifying energy conversion for systems that are not in local thermodynamic equilibrium (LTE), as is common in a number of fluid and plasma systems. Here,

Manuel de la Cruz-López, Jhony A. Herrera-Mendoza, Roberto Cartas-Fuentevilla, Alfredo Herrera-Aguilar

In this study, we investigate a type-II holographic superconductor with a perturbative scalar field over a (3 + 1)-dimensional electric and magnetically charged planar AdS black hole. After consistently decoupling the scalar field sector from the complete Einstein-Maxwell-Scalar system, we delve into the thermodynamical properties of the background relevant

Zhiwei Zhao, Changqing Liu, Yingguang Li, Zhibin Chen

In scientific and engineering applications, solving partial differential equations (PDEs) across various parameters and domains normally relies on resource-intensive numerical methods. Neural operators based on deep learning offered a promising alternative to PDEs solving by directly learning physical laws from data. However, the current neural operator meth

Evan E. Schneider, S. Alwin Mao

We present the fifth simulation in the CGOLS project -- a set of isolated starburst galaxy simulations modeled over large scales ($10\kpc$) at uniformly high resolution ($\Delta x \approx 5\pc$). Supernova feedback in this simulation is implemented as a disk-wide distribution of clusters, and we assess the impact of this geometry on several features of the r

Dmitry V. Makhov, Gregory Armstrong, Hsiao-Han Chuang, Harin Ambalampitiya

The process of dissociation for two hydrofluorocarbon molecules in low triplet states excited by electron impact in plasma is investigated by ab initio Molecular Dynamics (AIMD). The interest in dissociation of hydrofluorocarbons in plasma is motivated by their role in plasma etching in microelectronic technologies. Dissociation of triplet states is very fas

George A. Blaylock-Squibbs, Richard J. Parker

Observations of star-forming regions provide snapshots in time of the star formation process, and can be compared with simulation data to constrain the initial conditions of star formation. In order to make robust inferences, different metrics must be used to quantify the spatial and kinematic distributions of stars. In this paper, we assess the suitability

Ananya Mondal, Greg Morrison

Single-molecule force-extension experiments have allowed quantitative measurements of the mechanical responses of biomolecules to applied forces explaining their roles in key biological functions. Electrophoretic stretching of charged polymers such as DNA in uniform electric fields is one such example, currently, used for sequencing purposes. Field-extension

Afnan Shatat

Photonuclear reactions are induced by the strong electromagnetic field generated by ultrarelativistic heavy-ion collisions. These processes have been extensively studied in ultraperipheral collisions, where the impact parameter is larger than twice the nuclear radius. In recent years, the observation of coherent $J/\psi$ photoproduction at very low transvers

Siyuan Wang, Andrey Polyakov, Min Li, Gang Zheng

The objective of the invariant ellipsoid method is to minimize the smallest invariant and attractive set of a linear control system operating under the influence of bounded external disturbances. In this paper, this method is extended into the leader-following consensus problem. Initially, a linear control protocol is designed for the Multi-agent System with

Arlindo F. da Conceição, Roman Vitenberg

This article discusses the implementation of programmable money on DLT-based CBDCs. After briefly introducing what programmable money is, we enumerate some initiatives worldwide and discuss the critical steps for implementation. We look at the challenges from the Computer Science perspective. Four aspects were analyzed: architectural design, security, scalab

Taylor Daniels

Let $f:\mathbb{N}\to\{0,\pm 1\}$, for $n \in \mathbb{N}$ let $\Pi[n]$ be the set of partitions of $n$, and for all partitions $\pi = (a_1,a_2,\ldots,a_k) \in \Pi[n]$ let \[ f(\pi) := f(a_1)f(a_2) \cdots f(a_k). \] With this we define the $f$-signed partition numbers \[ \mathfrak{p}(n,f) = \sum_{\pi\in\Pi[n]} f(\pi). \] In this paper, for odd primes $p$ we de

Hitesh Vaidya, Travis Desell, Ankur Mali, Alexander Ororbia

An intelligent system capable of continual learning is one that can process and extract knowledge from potentially infinitely long streams of pattern vectors. The major challenge that makes crafting such a system difficult is known as catastrophic forgetting - an agent, such as one based on artificial neural networks (ANNs), struggles to retain previously ac

Mauricio S. Louzeiro, Gilson N. Silva, Jinyun Yuan, Daoping Zhang

A quasi-Newton method with cubic regularization is designed for solving Riemannian unconstrained nonconvex optimization problems. The proposed algorithm is fully adaptive with at most ${\cal O} (\epsilon_g^{-3/2})$ iterations to achieve a gradient smaller than $\epsilon_g$ for given $\epsilon_g$, and at most $\mathcal O(\max\{ \epsilon_g^{-\frac{3}{2}}, \eps

T. Pauletti, M. Sanino, L. Gimenes, I. M. Carvalho

In the realm of quantum chemistry, the accurate prediction of electronic structure and properties of nanostructures remains a formidable challenge. Density Functional Theory (DFT) and Density Matrix Renormalization Group (DMRG) have emerged as two powerful computational methods for addressing electronic correlation effects in diverse molecular systems. We co

Marco Frasca, Anish Ghoshal, Nobuchika Okada

We propose a novel pathway to generate the electroweak scale (EW) via non-perturbative dynamics of a dark gauge sector based on the SU(N) gauge group. Imposing the scale invariance of the theory, we investigate the electroweak symmetry breaking (EWSB) which is triggered dynamically via the condensation of gauge fields. Instead of the usual dimension-4 trigge

Christopher J. Lindsay, J. M. Joel Ong, Sarbani Basu

Some physical processes that occur during a star's main-sequence evolution also affect its post main-sequence evolution. It is well known that stars with masses above approximately 1.1 $M_{\odot}$ have well-mixed convective cores on the main sequence, however, the structure of the star in the neighborhood of the convective core regions is currently undercons

Claudio Andrea Manzari, Dean J. Robinson

We develop a new theoretical framework for the treatment of heavy quark (HQ) resonances within heavy quark effective theory (HQET). This framework uses on-shell recursion techniques to express the resonant amplitude as a product of on-shell subamplitudes, which allows one to employ a form-factor representation of the hadronic matrix elements and to obtain an

Athanasios Bakopoulos, Thanasis Karakasis, Nick E. Mavromatos, Theodoros Nakas

We consider higher-order derivative gauge field corrections that arise in the fundamental context of dimensional reduction of String Theory and Lovelock-inspired gravities and obtain an exact and asymptotically flat black-hole solution, in the presence of non-trivial dilaton configurations. Specifically, by considering the gravitational theory of Euler-Heise

D. A. Fagundes, M. V. T. Machado

In this paper, we analyze the application of an analytical gluon distribution based on double-asymptotic scaling to the photoproduction of vector mesons in coherent $pp$, $pA$, and $AA$ collisions at LHC energies, using the color dipole formalism. Predictions for the rapidity distribution are presented for $\rho^0$, $J/ \psi$, $\psi (2S)$, and $\Upsilon (1S)

Aakash, Indranil Saha

The fundamental goal assignment problem for a multi-robot application aims to assign a unique goal to each robot while ensuring collision-free paths, minimizing the total movement cost. A plausible algorithmic solution to this NP-hard problem involves an iterative process that integrates a task planner to compute the goal assignment while ignoring the collis

Christina Giannitsi, Nazar Miheisi, Hamed Mousavi

We discuss the Pointwise Ergodic Theorem for the Gaussian divisor function $d(n)$, that is, for a measure preserving $\mathbb Z[i]$ action $T$, the limit $$\lim_{N\rightarrow \infty} \frac{1}{D(N)} \sum _{\mathscr{N} (n) \leq N} d(n) \,f(T^n x) $$ converges for every $f\in L^p$, where $\mathscr{N} (n) = n \bar{n}$, and $D(N) = \sum _{\mathscr{N} (n) \leq N}

Kushal Chakraborty, Aakash Kumar, Arnab Rudra, Amey Yeole

We constructed all possible kinematically allowed three-point interactions of two massless Dirac spinors with massive higher-spin bosons. In any $D$ spacetime, the interactions have been constructed using the projections of the higher spin irreducible representations of $Spin(D-1)$ over the product of two irreducible spinor representations of $Spin(D-2)$. Ba

Razvan Gabriel Iagar, Ariel Sánchez

We establish the existence of self-similar solutions presenting finite time blow-up to the quasilinear reaction-diffusion equation $$ u_t=\Delta u^m + u^p, $$ posed in dimension $N\geq3$, $m>1$. More precisely, we show that there is always at least one solution in backward self-similar form if $p>p_s=m(N+2)/(N-2)$. In particular, this establishes \emph{non-o

Luca Di Luzio, Alfredo Walter Mario Guerrera, Xavier Ponce Díaz, Stefano Rigolin

Radiative quarkonia decays offer an ideal setting for probing Axion-Like Particle (ALP) interactions. This paper provides a comprehensive review of ALP production mechanisms through the $e^+ e^- \to \gamma\,a$ process at B- and Charm-factories, alongside an analysis of potential ALP decay channels. We derive constraints on ALP couplings to Standard Model (SM

Clay Cordova, Sungwoo Hong, Seth Koren

We consider theories of gauged quark flavor and identify non-invertible Peccei-Quinn symmetries arising from fractional instantons when the resulting gauge group has non-trivial global structure. Such symmetries exist solely because the Standard Model has the same numbers of generations as colors, $N_g = N_c$. This leads us to a massless down-type quark solu

Igor Palubski, Oren Slone, Manoj Kaplinghat, Mariangela Lisanti

When dark matter has a large cross section for self scattering, halos can undergo a process known as gravothermal core collapse, where the inner core rapidly increases in density and temperature. To date, several methods have been used to implement Self-Interacting Dark Matter~(SIDM) in N-body codes, but there has been no systematic study of these different

Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli

Connecting text and visual modalities plays an essential role in generative intelligence. For this reason, inspired by the success of large language models, significant research efforts are being devoted to the development of Multimodal Large Language Models (MLLMs). These models can seamlessly integrate visual and textual modalities, while providing a dialo

F. Yusef-Zadeh, Jun-Hui Zhao, R. Arendt, M. Wardle

We investigate the nature of a Galactic center source, G0.17+0.15, lying along the northern extension of the Radio Arc near l~0.2deg. G0.17+0.15 is an HII region located toward the eastern edge of the radio bubble, embedded within the highly polarized Galactic center eastern Lobe where a number of radio filaments appear to cross through the HII region. We re

Akanksha Bhardwaj, Christoph Englert, Wrishik Naskar, Vishal S. Ngairangbam

This study introduces a novel Graph Neural Network (GNN) architecture that leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of collider data for Beyond the Standard Model (BSM) discoveries. By integrating equivariance in the rapidity-azimuth plane with IRC-safe principles, our model significantly reduces computational ov

Alessandro Ruzza, Giuseppe Lodato, Giovanni Pietro Rosotti

Current methods to characterize embedded planets in protoplanetary disc observations are severely limited either in their ability to fully account for the observed complex physics or in their computational and time costs. To address this shortcoming, we developed DBNets: a deep learning tool, based on convolutional neural networks, that analyses substructure

Gregoire Marc

Suppose $G$ is a finite group. In this paper, we construct an equivalence between the $\infty$-category of algebras over an $N_{\infty}$-operad $\mathcal{O}$ associated to a $G$-indexing system $\mathcal{I}$ and the corresponding $\infty$-category of higher incomplete $\mathcal{I}$-Mackey functors with value in spaces. We use the universal property of the in

V. Vilasini, Roger Colbeck

A central goal in the foundations of physics is to understand the structure of physical theories, such as quantum theory, from physical principles. This is often explored by considering various information-theoretic principles. Here, we initiate a similar approach considering relativistic causality principles. No superluminal causation (NSC) and no superlumi

Federico Settimo, Kimmo Luoma, Dariusz Chruściński, Bassano Vacchini

The dynamics of open quantum systems is often solved by stochastic unravellings where the average over the state vector realizations reproduces the density matrix evolution. We focus on quantum jump descriptions based on the rate operator formalism. In addition to displaying and exploiting different equivalent ways of writing the master equation, we introduc

Claire S. Ye, Maya Fishbach

Gravitational-wave detectors are unveiling a population of binary black hole (BBH) mergers out to redshifts $z \approx 1$, and are starting to constrain how the BBH population evolves with redshift. We present predictions for the redshift evolution of the BBH mass and spin distributions for systems originating from dense star clusters. Utilizing a grid of 14

Alis J. Deason, Vasily Belokurov

The Gaia mission has revolutionized our view of the Milky Way and its satellite citizens. The field of Galactic Archaeology has been piecing together the formation and evolution of the Galaxy for decades, and we have made great strides, with often limited data, towards discovering and characterizing the subcomponents of the Galaxy and its building blocks. No

S. de Wit, A. Z. Bonanos, K. Antoniadis, E. Zapartas

Mass loss during the red supergiant (RSG) phase plays a crucial role in the evolution of an intermediate massive star, however, the underlying mechanism remains unknown. We aim to increase the sample of well-characterized RSGs at subsolar metallicity, by deriving the physical properties of 127 RSGs in nine nearby, southern galaxies presented by Bonanos et al

F. Yusef-Zadeh, Jun-Hui Zhao, R. Arendt, M. Wardle

The Snake is a remarkable Galactic center radio filament with a morphology characterized by two kinks along its $\sim 20'$ extent. The major and minor kinks are located where the filament is most distorted from a linear magnetized structure running perpendicular to the Galactic plane. We present {\em Chandra}, VLA, and MeerKAT data and report the detection o

Ilin Lazar, Sugata Kaviraj, Aaron E. Watkins, Garreth Martin

We use a complete, unbiased sample of 257 dwarf (10^8 MSun < Mstar < 10^9.5 MSun) galaxies at z < 0.08, in the COSMOS field, to study the morphological mix of the dwarf population in low-density environments. Visual inspection of extremely deep optical images and their unsharp-masked counterparts reveals three principal dwarf morphological classes. 43 and 45

Laura Scholz-Diaz, Ignacio Martin-Navarro, Jesus Falcon-Barroso, Mariya Lyubenova

In the standard cosmological model, the assembly of galaxies is primarily driven by the growth of their host dark matter halos. At the center of these halos, however, baryonic processes take over, leading to the plethora of observed galaxy properties. The coupling between baryonic and dark matter physics is central to our understanding of galaxies and yet, i

K. Sharpe, L. A. C. van Son, S. E. de Mink, R. Farmer

Chemically homogeneous evolution (CHE) is a promising channel for forming massive binary black holes. The enigmatic, massive Wolf-Rayet (WR) binary HD 5980 A&B has been proposed to have formed through this channel. We investigate this claim by comparing its observed parameters with CHE models. Using MESA, we simulate grids of close massive binaries then use

Roberto Casadio, Leonardo Chataignier, Alexander Yu. Kamenshchik, Francisco G. Pedro

We make a conceptual overview of a particular approach to the initial-value problem in canonical gauge theories. We stress how the first-class phase-space constraints may be relaxed if we interpret them as fixing the values of new degrees of freedom. This idea goes back to Fock and Stueckelberg, leading to restrictions of the gauge symmetry of a theory, and

Zhengyan Darius Shi, Hart Goldman, Zhihuan Dong, T. Senthil

We study a family of excitonic quantum phase transitions describing the evolution of a bilayer metallic state to an inter-layer coherent state where excitons condense. We argue that such transitions can be continuous and exhibit a non-Fermi liquid counterflow response ${\rho_{\mathrm{counterflow}}(\omega)\sim\omega^{2/z}}$ that directly encodes the dynamical

Lorenzo Branca, Andrea Pallottini

Galaxy formation and evolution critically depend on understanding the complex photo-chemical processes that govern the evolution and thermodynamics of the InterStellar Medium (ISM). Computationally, solving chemistry is among the most heavy tasks in cosmological and astrophysical simulations. The evolution of such non-equilibrium photo-chemical network relie

Nils Quetschlich, Mathias Soeken, Prakash Murali, Robert Wille

Quantum computing has made considerable progress in recent years in both software and hardware. But to unlock the power of quantum computers in solving problems that cannot be efficiently solved classically, quantum computing at scale is necessary. Unfortunately, quantum simulators suffer from their exponential complexity and, at the same time, the currently

Vivian Yun Yan Tan, Adam Muzzin, Danilo Marchesini, Visal Sok

The resolved mass assembly of Milky-Way-mass galaxies has been previously studied in simulations, the local universe, and at higher redshifts using infrared (IR) light profiles. To better characterize the mass assembly of Milky Way Analogues (MWAs), as well as their changes in star-formation rate and color gradients, we construct resolved stellar mass and st

Jehyun Kim, Himanshu Dev, Ravi Kumar, Alexey Ilin

Quasi-particles in fractional quantum Hall states are collective excitations that carry fractional charge and anyonic statistics. While the fractional charge affects semi-classical characteristics such as shot noise and charging energies, the anyonic statistics is most notable in quantum interference phenomena. In this study, we utilize a versatile bilayer g

Amelie Wührl, Dustin Wright, Roman Klinger, Isabelle Augenstein

Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasing volume of science communication in recent years, a fine-grained understanding of how findings from scientific publications are reported to the general public, and methods to dete

Kara Farnsworth, Kurt Hinterbichler, Ondrej Hulik

We examine the question of scale versus conformal invariance on maximally symmetric curved backgrounds and study general 2-derivative conformally invariant free theories of vectors and tensors. For spacetime dimension $D>4$, these conformal theories can be diagonalized into standard massive fields in which unbroken conformal symmetry non-trivially mixes comp

Dany Atallah, Newlin C. Weatherford, Alessandro A. Trani, Frederic Rasio

We explore three-body binary formation (3BBF), the formation of a bound system via gravitational scattering of three initially unbound bodies (3UB), using direct numerical integrations. For the first time, we consider systems with unequal masses, as well as finite-size and post-Newtonian effects. Our analytically derived encounter rates and numerical scatter

George P. Prodan, Marcel Popescu, Javier Licandro, Mohammad Akhlaghi

The discovery of interstellar comet 2I/Borisov offered the unique opportunity to obtain a detailed analysis of an object coming from another planetary system, and leaving behind material in our interplanetary space. We continuously observed 2I/Borisov between October 3 and December 13, 2019 using the 1.52-m Telescopio Carlos S\'{a}nchez equipped with MuSCAT2

Tommaso Ronconi, Andrea Lapi, Martina Torsello, Alessandro Bressan

Fostered by upcoming data from new generation observational campaigns, we are about to enter a new era for the study of how galaxies form and evolve. The unprecedented quantity of data that will be collected, from distances only marginally grasped up to now, will require analysis tools designed to target the specific physical peculiarities of the observed so

Phatthamon Kongkhambut, Jayson G. Cosme, Jim Skulte, Michelle A. Moreno Armijos

Discrete (DTCs) and continuous time crystals (CTCs) are novel dynamical many-body states, that are characterized by robust self-sustained oscillations, emerging via spontaneous breaking of discrete or continuous time translation symmetry. DTCs are periodically driven systems that oscillate with a subharmonic of the external drive, while CTCs are continuously

Christian Reiser, Stephan Garbin, Pratul P. Srinivasan, Dor Verbin

While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensive methods that model the scene's geometry as a volumetric density field (e.g. NeRF) excel at reconstructing fine geometric detail. However, density fields often represent geometry

Zeyu Lu, Zidong Wang, Di Huang, Chengyue Wu

Nature is infinitely resolution-free. In the context of this reality, existing diffusion models, such as Diffusion Transformers, often face challenges when processing image resolutions outside of their trained domain. To overcome this limitation, we present the Flexible Vision Transformer (FiT), a transformer architecture specifically designed for generating

Paola Domínguez-Fernández, Dongsu Ryu, Hyesung Kang

Recent observations have revealed detailed structures of radio relics in a wide range of frequencies. In this work, we perform three-dimensional magnetohydrodynamical simulations of merger shocks propagating through a turbulent magnetized intracluster medium, and employ on-the-fly Lagrangian particles to explore the physical processes originating radio subst

Zhuoming Chen, Avner May, Ruslan Svirschevski, Yuhsun Huang

As the usage of large language models (LLMs) grows, performing efficient inference with these models becomes increasingly important. While speculative decoding has recently emerged as a promising direction for speeding up inference, existing methods are limited in their ability to scale to larger speculation budgets, and adapt to different hyperparameters an

Mojtaba Valizadeh, Nathanaël Fijalkow, Martin Berger

Linear temporal logic (LTL) is widely used in industrial verification. LTL formulae can be learned from traces. Scaling LTL formula learning is an open problem. We implement the first GPU-based LTL learner using a novel form of enumerative program synthesis. The learner is sound and complete. Our benchmarks indicate that it handles traces at least 2048 times

Mario Sänger, Samuele Garda, Xing David Wang, Leon Weber-Genzel

With the exponential growth of the life science literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. Identifying named entities (e.g., diseases, drugs, or genes) in texts and their linkage to reference knowledge bases are crucial steps in BTM pipelines to enable information

Bernd Gärtner, Fatime Rasiti, Patrick Schnider

Enclosing depth is a recently introduced depth measure which gives a lower bound to many depth measures studied in the literature. So far, enclosing depth has only been studied from a combinatorial perspective. In this work, we give the first algorithms to compute the enclosing depth of a query point with respect to a data point set in any dimension. In the

Xiao Ye, Andrew Wang, Jacob Choi, Yining Lu

Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer this question, we propose AnaloBench, a

Greg Olmschenk, Richard K. Barry, Stela Ishitani Silva, Brian P. Powell

The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~85% of the sky throughout its two-year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast dataset, we aim to provide an approach that is both computationally efficient, produ

Mohammad Javad Hosseini, Andrey Petrov, Alex Fabrikant, Annie Louis

Natural Language Inference (NLI) remains an important benchmark task for LLMs. NLI datasets are a springboard for transfer learning to other semantic tasks, and NLI models are standard tools for identifying the faithfulness of model-generated text. There are several large scale NLI datasets today, and models have improved greatly by hill-climbing on these co

E. Kyritsis, A. Zezas, F. Haberl, P. Weber

We present a study of X-ray normal galaxies using data from the first all-sky scan of the eROSITA X-ray survey. eRASS1 provides the first unbiased X-ray census of normal galaxies allowing us to study the X-ray emission from XRBs and the hot ISM in the full range of stellar population parameters present in the local Universe. By combining the HECATE value-add

Archit Sharma, Sedrick Keh, Eric Mitchell, Chelsea Finn

Reinforcement learning with AI feedback (RLAIF) is a popular paradigm for improving the instruction-following abilities of powerful pre-trained language models. RLAIF first performs supervised fine-tuning (SFT) using demonstrations from a teacher model and then further fine-tunes the model with reinforcement learning (RL), using feedback from a critic model.

Ryan Swann, Muhammad Osama, Karthik Sangaiah, Jalal Mahmud

Modern GPUs are designed for regular problems and suffer from load imbalance when processing irregular data. Prior to our work, a domain expert selects the best kernel to map fine-grained irregular parallelism to a GPU. We instead propose Seer, an abstraction for producing a simple, reproduceable, and understandable decision tree selector model which perform

Benedikt Alkin, Andreas Fürst, Simon Schmid, Lukas Gruber

Neural operators, serving as physics surrogate models, have recently gained increased interest. With ever increasing problem complexity, the natural question arises: what is an efficient way to scale neural operators to larger and more complex simulations - most importantly by taking into account different types of simulation datasets. This is of special int